Automatic segmentation of peripheral arteries and veins in ferumoxytol-enhanced MR angiography.
Automatic segmentation of peripheral arteries and veins in ferumoxytol-enhanced MR angiography.
复制标题
费鲁莫托增强磁共振血管造影中外周动脉和静脉的自动分割。
DOI：
10.1002/mrm.29026
复制
发表时间：
2022
影响因子：
3.3
通讯作者：
Hu,Peng
中科院分区：
文献类型：
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作者：
Ghodrati,Vahid;Rivenson,Yair;Prosper,Ashley;deHaan,Kevin;Ali,Fadil;Yoshida,Takegawa;Bedayat,Arash;Nguyen,Kim-Lien;Finn,JPaul;Hu,Peng
目的基于阿魏酸甘油酯增强磁共振血管成像(FE-MRA)，实现对下肢外周动脉和静脉的自动分割。在第一阶段，我们使用带有局部关注门的3D U-Net，该U-Net是在深度监督机制下基于焦点Tversky损失和区域互损失相结合的训练来从高分辨率FE-MRA数据集中分割血管系统。在第二阶段，我们使用时间分辨图像来分离动脉和静脉。由于动脉和静脉的最终分割质量取决于第一阶段的性能，我们对分割网络的不同方面进行了全面的评估，并将其在血管分割中的性能与目前公认的最先进的网络进行了比较，包括Volumeter-Net、DeepVesselNet-FCN和Uceptions。结果对于血管分割，我们获得了具有竞争力的F1=0.8087，召回率=0.8410，而体积网、DeepVesselNet-FCN和Uept的分割结果分别为F1=(0.7604，0.7573，0.7651)和Recall=(0.7791，0.7570，0.7774)。对于动静脉分离阶段，我们在周围动静脉分割中最具挑战性的小腿区域实现了F1=(0.8274/0.7863)。结论该管道能够在4min内实现基于FE-MRA的全自动血管分割，而不需要人工干预。这种方法改进了放射科医生的人工分割，后者通常需要几个小时。
PurposeTo automate the segmentation of the peripheral arteries and veins in the lower extremities based on ferumoxytol‐enhanced MR angiography (FE‐MRA).MethodsOur automated pipeline has 2 sequential stages. In the first stage, we used a 3D U‐Net with local attention gates, which was trained based on a combination of the Focal Tversky loss with region mutual loss under a deep supervision mechanism to segment the vasculature from the high‐resolution FE‐MRA datasets. In the second stage, we used time‐resolved images to separate the arteries from the veins. Because the ultimate segmentation quality of the arteries and veins relies on the performance of the first stage, we thoroughly evaluated the different aspects of the segmentation network and compared its performance in blood vessel segmentation with currently accepted state‐of‐the‐art networks, including Volumetric‐Net, DeepVesselNet‐FCN, and Uception.ResultsWe achieved a competitive F1 = 0.8087 and recall = 0.8410 for blood vessel segmentation compared with F1 = (0.7604, 0.7573, 0.7651) and recall = (0.7791, 0.7570, 0.7774) obtained with Volumetric‐Net, DeepVesselNet‐FCN, and Uception. For the artery and vein separation stage, we achieved F1 = (0.8274/0.7863) in the calf region, which is the most challenging region in peripheral arteries and veins segmentation.ConclusionOur pipeline is capable of fully automatic vessel segmentation based on FE‐MRA without need for human interaction in <4 min. This method improves upon manual segmentation by radiologists, which routinely takes several hours.